Biologically Inspired Graph Analysis of the WordNet English Database
Introduction:
We adopt a framework of parcels, analogous to a surface area of the neo-cortex
Ongoing biological measurements of neural function have yielded considerable insights into the
functioning of
Biological neural systems
Mammals and Reptiles inherited the cerebellum and limbic systems from their common ancestor, with
Mammals going on to develop the neo-cortex which allows for more complex behavior.
Neo-cortex grew out of limbic (… ridge)
Figure X cerebellum neurons, a single neuron can connect to many other neurons by the long parallel
axons
Neurons have suppression function, can be suppressed by another neuron firing
Figure X Neo-cortex has a layered structure. Layers present and largely similar in all Mammals.
Mammals split from reptiles ~350 Million years. Obviously something extremely important about this
architecture. The ability to develop parcels with specialized functions.
Any system of neurons can be remapped to a different shape. But systems that are biologically easily
accessible to evolution will presumably develop first.
Our understanding of the structure and functioning of the mammalian neo-cortex has expanded
considerably in recent decades, however there are likely many unknown unknowns remaining. We can
however explore and expropriate any understanding we can.
Flat sheet divided up into many specialized parcels. Axons interconnect between parcels. The grey
matter is a flat sheet, containing the 6 cortical layers. The white matter is the axons running between
parcels and other parts of the body.
Visual processing takes ~60% of neo-cortex in mammals. Complex, ordered hierarchy(s) of parcels are
observable by numerous methods.
The visual system parcels V1,V2,V3,… have many specialized neural circuits that are replicated spatially
in a grid like fashion, similar to a digital camera. Also observe spatially oriented axon connections
between parcels, indicating some method of axon guidance during growth.
Visual system, motion detection in V1 requires neural memory circuit (show rolling circuit). Take as
given that evolution can find easily accessible solutions to problems. V2 has repetitive structure of
neural functional units replicated in a grid, mapped to V1. Each functional unit performs a set of visual
processing functions on the input from V1.
Neo-cortex split into many specialized parcels
Complex neural circuits that implement specific functions are observed through direct measurement of
individual neuron firing
Section 2:
A neural circuit can be represented computationally as a graph of nodes
Node class is the Markov chain node. Weight variable is stored locally (chemically) for each branch.
Neural clusters form symbols (cellular clusters, Hebb 53)
A cluster firing represents the symbol being used in a thought process
Figure X Sensory input into a hypothetical parcel
Figure X: a cluster can be represented computationally as a single node
Node class is modified to:
Figure X: Each node is givens a string to represent it’s symbol. The node’s branches are indexed in a
map. Input and threshold variables are added to simulate firing in real time.
Many algorithms can be implemented in the Node class using only run time local information.
Code flags programmed into node class run at fire() time
AI becomes a reverse engineering problem, using the assumed graph structure.
Derive parcel hierarchy in main() using local algorithms by intelligent design.
Use Wordnet sentence corpus as input and build a parcel/graph hierarchy in main()
Assume that any local algorithms are possible and available for use.
Analogous biological algorithms will be easily accessible via evolution.
Lines and counting sequences occur through Hebb sorting.
1,2,3,… node sequence assumed to exist between parcels for control. – dorsal pathway?
Assume that the “Sparse STM” memory model allows any two symbols in a parcel to be associated
together temporarily. When two symbols (nodes) fire simultaneously, STM nodes connect the two
symbol nodes.
STM linking between number line and nodes in a parcel would be possible. This allows variable binding.
Would also be more efficient, each symbol just has to have STM nodes to number line, not to all other
symbols.
the parcel/stm/line model
Figure X: the parcel/stm/line model, input = / man / eat / meat /
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IO-parcel is top level parcel. All known input/output symbols are formed in the IO-parcel (?).
Nodes can grow axons to a new parcel and draft a free neuron there to represent that symbol in
the new parcel. The symbols in IO bifurcate into separate Noun and Verb parcels. Number line
assumed to exist between parcels. Symbols can STM bind to number line
The ability to prepare-for a sequence of actions, and hold the sequence until a trigger occurs; cat
springing on prey. Pre-loading a dorsal parcel
This would drive the parcel/stm/line model
Many other programmed tasks in dorsal parcels, math problem,…
Learning is done by creating new LTM nodes and connections on the existing graphs. Cortex is always
recording by adding nodes.
Every sequence of symbols that fire in IO causes STM nodes to be added.
So called statistical techniques such as deep hierarchical networks rely on fine tuning large numbers of
weights between many pre-existing nodes (10^6?)
Derive POS parcels:
List of words in Wordnet corpus sorted by frequency:
In main(), manually form a conjunctive node between the “a” and “the” nodes, label as: (a:the)
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(a:the) node can then grow to new parcel
Upon further corpus input, nodes (i.e. words) that fire after “a” or “the” in the IO parcel can
grow axons to the (a:the) parcel, via some assumed axonal guidance mechanism, and recruit a
free node there
This allows us to build a parcel that contains the nouns and adjectives
Using a similar procedure, we can mine out the prepositions and verbs from nodes that have (a:the) as a
branch (the head nodes of (a:the)).
The verbs and prepositions can be separated by ID mining of the sorted list of head nodes of (a:the).
Can then separate the nouns+adj parcel. The sequences: “(preposition) (adj) (N)” and “(preposition)
(N)” are observed in the corpus, but the sequence “(preposition) (adj)” never occurs. We can use this
to separate the nouns and adjectives in the (a:the) parcel above.
Build parcel/node pathway:
Links are assumed to be capable of forming bi-directionally.
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All inputable/outputable symbols exist in IO parcel
symbols grow to new parcels by some logic (hand made by ID or biological algorithm)
seed node of a parcel can grow to other parcels (noun,verbCSK)
can aggregate high level nodes together in separate parcel
allows LTM memory node to form between sparse nodes in upper parcel
also CSK takes the form of sequences of POS nodes, this can be used to generate a “FALSE”
signal when an illegal or unknown sequence form is input, such as “verb verb”
sequence recall from memory node (sequences stored higher
ventral pathways, not dominated by 1,2,3,… line
Filtering
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provide filter function,
Input = “man eat”
STM nodes in IO form to temporarily store the input sequence
Could be direct, single, or double conjunctive
Need at least single conjunctive to store a sequence
STM node are time stamped and are expired on a later call to touch()
STM nodes are implemented as flag/function nodes
Understanding input symbols using CSK: noun-verb
Understanding input symbols by association:
Ignore temporal sequence, consider nodes fired at same time in IO parcel
Assume an association between “noun” and “verb” is formed in an LTM memory parcel. Nodes are
directly connected in LTM memory parcel
With STM node linking m1 and e1 in the IO parcel, a complete loop exists on the graph
Single hypernym understand.
(is a) hypernym parcel adds additional way to understand input.
Associates nouns together
(is a) is a verb parcel
The “I” parcel:
All things related to the individual. This parcel fires easily, lower threshold. Important for organism to
interpret ongoing sensory input in relation to itself.
Process is to continue to further bifurcate parcels. Done manually in main(). One goal is to convert this
process to text so that it can be widely deployed, i.e. interacting with humans interacting with it on text
forums.
Human sequence interactions (conversations) are stored on existing parcel/graph hierarchy. Human IDs
can aggregate information and build new parcels.
Sequence recall
Recall similar sequence and run through IO
Prepositions
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() prefixed nodes in diagram are STM nodes in each parcel, can expire and be deleted at
subsequent run time
after “walk in city” is input, the STM memory nodes in IO are populated as shown( by “()” ). These store
the raw sequence, and allow the raw sequence to be re-generated.
STM function implemented as a code-flag node:
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pass on input
Fatigue
STM links get set in the in-parcel as shown. If these were set in the in-parcel, and not in the IO-parcel,
then “walk in city” could be generated from the “in” parcel.
Prepositional phrases are similar to adj/adv in that they add detail to a noun or a verb. Called from the
n/v ?
“in” parcel shown with two archetype nodes.
Verbs add detail to a noun
Sequence recall
Output phrasing, a/the hurt-ed/hurt
Verb parcels,
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Is Parcel:
Irregular forms of “is” increase the accuracy of information reception
“I is hungry” is just one symbol away from: “It is hungry”
(is a) noun hypernym parcel hierarchy, exposes “a” and “the” differences
morphemes
noun verb equivalence
Introduce global time steps
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Nodes that fire touch their branches
If any branch node is over threshold on a touch() call, it will fire on the next time cycle
Hebb: weight- - when touching, ++ if node fires, nodes keep list of touches
Wordnet gloss sentences, sorted:
The most frequently encoded relation among synsets is the super-subordinate relation (also
called hyperonymy, hyponymy or ISA relation).
It links more general synsets like {furniture, piece_of_furniture} to increasingly specific ones like
{bed} and {bunkbed}.
Thus, WordNet states that the category furniture includes bed, which in turn includes bunkbed;
conversely, concepts like bed and bunkbed make up the category furniture.
All noun hierarchies ultimately go up the root node {entity}.
After firing, neural cluster (can) remain active for short time
Parcel: collection of symbols
STM neurons can connect any two symbols together within a parcel
/ a / b / ….. / a / b
http://www.psy.vanderbilt.edu/faculty/roeaw/pdfs/Roe03Chapter.pdf
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? Pattern Analysis
and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 8 )
Hardcopy
George A. Miller (1995). WordNet: A Lexical Database for English.
Communications of the ACM Vol. 38, No. 11: 39-41.
Christiane Fellbaum (1998, ed.) WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.
WordNet: An Electronic Lexical Database (citation above) is available from MIT Press.
Online
Publications should cite this website when referring to the online version of WordNet. Below is a citation
in MLA web citation style.
Princeton University "About WordNet." WordNet. Princeton University. 2010.
<http://wordnet.princeton.edu>